There are two lectures each week: Tuesday 9.00-10.45 hrs and Thursday 13.15-15.00 hrs.
In the planning below, lecture 37A is the Tuesday lecture in week 37 etc.
There are Computer Lab sessions (P) in weeks 39-43.
| Lecture | Subject/Slides | Literature |
|---|---|---|
| 37A | Introduction (Slides) | A. Feelders, H. Daniels, M. Holsheimer Methodological and Practical Aspects of Data Mining |
| 37B | Classification Trees (1) (Slides) | Lecture notes Classification Trees: section 1-3.3 |
| 38A | Classification Trees (2) (Slides) | Lecture notes Classification Trees: section 3.4-3.5 |
| 38B | Clustering (Slides) | The Slides |
| 39A | Self-Organizing Maps (1) [Guest Lecture by Dr. Markus Schedl] (Slides) | The Slides |
| 39B | Self-Organizing Maps (2) [Guest Lecture by Dr. Markus Schedl] | The Slides |
| 39P | Computer Lab | Work on assignment 1 |
| 40A | Graphical Models (1) (Slides) | Lecture Notes Graphical Models (Part 1) |
| 40B | Graphical Models (2) (Slides) | Lecture Notes Graphical Models (Part 1) |
| 40P | Computer Lab | |
| 41A | Graphical Models (3)/Exercise Class (Exercises) | |
| 41B | Exercise Class (see 41A for exercises) (Solutions) | |
| 41P | Computer Lab | |
| 42A | Bayesian Networks (1) (Slides) | Lecture Notes on Graphical Models (Part 2): Sections 1-4 |
| 42B | Bayesian Networks (2) (Slides) | Lecture Notes on Graphical Models (Part 2): Sections 5-6 |
| 42P | Computer Lab | |
| 43A | Bayesian Network Classifiers (Slides) | Article N. Friedman et al. (see Literature) |
| 43B | Frequent Itemset Mining (Slides) | Lecture Notes Frequent Item Set Mining (see Literature). |
| 43P | Computer Lab | |
| 44A | Subgroup Discovery (Slides) | Lecture notes Rule induction by bump hunting (see Literature). Article J.H. Friedman and N.I. Fisher (see Literature) |
| 44B | Exercise Class: Exercises (Solutions) | |
| 44P | Computer Lab |